Volume 48, Issue 6 e70036
Research Article

An Electrochemical Detection of Malathion Pesticide Using Cu Electrode and Enhanced by Machine Learning

Ashirbad Khuntia

Ashirbad Khuntia

Department of Chemical Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, India

Department of Electronics and Communication Engineering, Centurion University of Technology and Management, Paralakhemundi, Odisha, India

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Madhusree Kundu

Corresponding Author

Madhusree Kundu

Department of Chemical Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, India

E-mail: [email protected]

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Kamalakanta Mahapatra

Kamalakanta Mahapatra

Department of Electronics and Communication Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, India

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Adhidesh Kumawat

Adhidesh Kumawat

Department of Chemical Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, India

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First published: 25 May 2025

Abstract

The present work demonstrates the development of an economical and user-friendly “copper rods” sensor for detecting malathion. Differential pulse voltammetry (DPV) was performed to observe the inhibition ratio at various concentrations of malathion, which increases with an increase in malathion concentration. The parameters like pH and accumulation time were optimized at 4 pH and 18 min, respectively, corresponding to the maximum inhibition ratio (ΔI/I0). The electrochemical sensor had a relative standard deviation (RSD) of up to 7.05 % (n = 3), which indicated reproducible results. The regression line showed linearity over a range of 25–200 parts per billion (ppb), and the limit of quantification (LOQ) was as low as 25 ppb (75.67 nM). The developed sensor was sensitive and selective, with a limit of detection (LOD) as low as 1 ppb (3.03 nM). The selectivity of the sensor was also studied by adding Pb(NO3)2, Zn(NO3)2, and NiCl2 to a solution of fixed malathion concentration, and minimal interference was observed. The sensor's functionality was validated using an unknown concentration of malathion with 96 % and 106 % recovery, respectively. The sensitivity of this proposed sensor was 0.0165 µA ppb−1. Quantification of malathion was also facilitated using partial least squares (PLS) algorithms utilizing the sensory measurements of the malathion-contaminated samples. PLS is a statistical machine learning algorithm that has been used here to develop a predictor for unknown malathion concentration using the DPV current signatures of the contaminated solution with a nominal error of 5.0 %.

Data Availability Statement

Data will be available upon reasonable request.

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